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// Copyright (C) 2002 Samy Bengio (bengio@idiap.ch)
// and Bison Ravi (francois.belisle@idiap.ch)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// Torch is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef BAYES_CLASSIFIER_MACHINE_INC
#define BAYES_CLASSIFIER_MACHINE_INC
#include "Machine.h"
#include "Trainer.h"
#include "ClassFormat.h"
namespace Torch {
/** BayesClassifierMachine is the machine used by the #BayesClassifier#
trainer to perform a Bayes Classification using different distributions.
The output corresponds to the class that is the most probable
(using prior AND posterior information).
@author Samy Bengio (bengio@idiap.ch)
@author Bison Ravi (francois.belisle@idiap.ch)
*/
class BayesClassifierMachine : public Machine
{
public:
/// the number of classes corresponds to the number of #Trainer#
int n_trainers;
/// the actual trainers.
Trainer** trainers;
/** the log_prior probabilities of each class. default: log_priors are
taken as the log of the proportions in the training set.
*/
real* log_priors;
/// contains the log posterior probability plus the log prior of the class
real* log_posteriors;
List* list_log_posteriors;
/// used to know if log_priors where given or allocated
bool allocated_log_priors;
/// the format of the data
ClassFormat* class_format;
/// the measurers for each individual trainer
List** trainers_measurers;
/** creates a machine for BayesClassifier trainers, given a vector of
trainers (one per class), an associate measurer for each trainer,
a class_format that explains how the classes are coded, and an eventual
vector (of size #n_trainers_#) containing the log of the class priors.
*/
BayesClassifierMachine( Trainer**, int n_trainers_, List** trainers_measurers_ , ClassFormat* class_format_, real* log_priors_=NULL);
virtual ~BayesClassifierMachine();
/** definition of virtual functions of #Machine# */
virtual void forward( List* inputs );
virtual void reset();
virtual void loadFILE( FILE* );
virtual void saveFILE( FILE* );
};
}
#endif
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